axe-cli 1.8.3

axe, yerrrr

axe: The Agent Built for Real Engineers

What it really means to be a 10x engineer—and the tool built for that reality.

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what do you mean by a "10x engineer"

The industry loves the lore of "10x engineer"—the lone genius who ships a new product in a weekend, the hacker who rewrites the entire stack in a caffeine-fueled sprint, the visionary who creates something from nothing.

That's not what a 10x engineer actually does.

The real 10x engineers aren't working on greenfield projects. They're not inventing new frameworks or building the next viral app. They're maintaining behemoth codebases where millions of users depend on their decisions every single day.

Their incentive structure is fundamentally different: "If it's not broken, don't fix it."

And with that constraint in mind, they ask a different question entirely:

"What truly matters for solving this particular problem, and how can I gain enough confidence to ship it reliably?"

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The Real Engineering Challenge

Idea creation is a human trait. Ideas arise from impulsive feelings, obstacles we encounter, problems we want to solve. Creating something new is exciting, visceral, immediate.

Maintaining something reliably over time requires a completely different pedigree—and it's by far more important than creating the idea itself.

Consider the reality:

• A production codebase with 100,000+ lines across hundreds of files
• Millions of users whose workflows depend on your system staying stable
• Years of accumulated complexity: edge cases, performance optimizations, backwards compatibility
• Distributed teams where no single person understands the entire system
• The cost of breaking things is measured in downtime, lost revenue, and user trust

In this environment, the questions that matter are:

• "If I change this function, what breaks?"
• "How does this data flow through the system?"
• "What are all the execution paths that touch this code?"
• "Where are the hidden dependencies I need to understand before refactoring?"

This is where most coding tools fail.

They're built for the weekend hackathon, the demo video, the "move fast and break things" mentality. They optimize for speed of creation, not confidence in maintenance.

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Enter axe

We built axe because we understood this problem intimately. Our team has been maintaining production systems at scale, and we needed a tool that matched the way real engineering actually works.

axe is built for large codebases. Not prototypes. Not "good enough for now" solutions.

It's built for the engineer who needs to:

• Understand a call graph before changing a function signature
• Trace data flow to debug a subtle state corruption
• Analyze execution paths to understand why a test fails in CI but not locally
• Perform impact analysis before refactoring to know exactly what depends on what

The core insight: To ship reliably in large codebases, you need precise understanding, not exhaustive reading.

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How axe Works: Precision Through Intelligence

Most coding tools take the brute-force approach: dump your entire codebase into the context window and hope the LLM figures it out.

This is backwards.

axe uses axe-dig, a 5-layer code intelligence engine that extracts exactly what matters for the task at hand:

┌──────────────────────────────────────────────────────────────┐
│ Layer 5: Program Dependence  → "What affects line 42?"      │
│ Layer 4: Data Flow           → "Where does this value go?"  │
│ Layer 3: Control Flow        → "How complex is this?"       │
│ Layer 2: Call Graph          → "Who calls this function?"   │
│ Layer 1: AST                 → "What functions exist?"      │
└──────────────────────────────────────────────────────────────┘

This isn't about saving tokens. It's about technical precision.

When you need to understand a function, axe-dig gives you:

• The function signature and what it does
• Forward call graph: What does this function call?
• Backward call graph: Who calls this function?
• Control flow complexity: How many execution paths exist?
• Data flow: How do values transform through this code?
• Impact analysis: What breaks if I change this?

Sometimes this means fetching more context, not less. When you're debugging a race condition or tracing a subtle bug through multiple layers, axe-dig will pull in the full dependency chain—because correctness matters more than brevity.

The goal isn't minimalism. The goal is confidence.

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A Real Example: Understanding Before Changing

Let's say you need to refactor a payment processing function in a production system.

The wrong approach (most tools):

1. Read the entire payment.py file (4,200 tokens)
2. Read related files the LLM thinks might be relevant (15,000+ tokens)
3. Make changes based on incomplete understanding
4. Hope nothing breaks

The axe approach:

# 1. Understand what this function does
chop context process_payment --project . --depth 2

# Result: ~175 tokens
# - Function signature
# - What it calls: validate_card, stripe.charge, db.save_transaction
# - Complexity: 3 decision points
# - Data flow: card → card_valid → charge → transaction

# 2. See who depends on this (impact analysis)
chop impact process_payment .

# Result: Shows exactly which functions call this
# - payment.py: update_subscription (line 134)
# - subscription.py: renew_subscription (line 45)
# - tests/test_payment.py: 8 test functions

# 3. Understand the full execution path
chop slice src/payment.py process_payment 89

# Result: Only the 6 lines that affect the return value
# Not the entire 180-line function

Now you can refactor with confidence. You know:

• What the function does
• What depends on it
• What execution paths exist
• What data flows through it

This is what enables reliable shipping.

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The axe-dig Difference: Side-by-Side Comparison

To demonstrate the precision advantage, we built a minimal CLI agent implementation with basic tools (grep, edit, write, shell) and compared it against the same agent with axe-dig tools.

Note: These are intentionally minimal implementations to show how phenomenal the axe-dig difference is.

Example 1: Basic Edit Operations

Left: Basic CLI agent with grep
Right: axe CLI with axe-dig

The difference is clear. The basic agent searches blindly, while axe-dig understands code structure and dependencies.

Example 2: Understanding Call Flow Tracers

When asked to explain how call flow tracking works, both agents found the context—but the results were dramatically different.

Left: Had to read the entire file after grepping for literal strings. 44,000 tokens.
Right: axe-dig used 17,000 tokens while also discovering:

• Call graphs for the decorator used on tracer functions
• Thread-safe depth tracking mechanisms
• How functions using this decorator actually work

axe-dig didn't just use fewer tokens—it provided better understanding of how the code flows.

Example 3: The Compounding Effect

The difference compounds with follow-up questions. When we asked about caller information:

Left: Started wrong, inferred wrong, continued wrong.
Right: Had more context and better understanding from the start, leading to precise answers.

This is why axe doesn't just optimize for token savings—it optimizes for what the code actually does and how it flows.

Example 4: Active Search vs. Passive Explanation

In the mlx-lm codebase, when asked how to compute DWQ targets:

Left: Explained the concept generically.
Right: axe CLI actively searched the codebase and found the actual implementation.

Precision means finding the answer in your code, not explaining theory.

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Token Efficiency: A Consequence, Not the Goal

Yes, axe-dig achieves 95% token reduction compared to reading raw files.

Scenario	Raw Tokens	axe-dig Tokens	Savings
Function + callees	21,271	175	99%
Codebase overview (26 files)	103,901	11,664	89%
Deep call chain (7 files)	53,474	2,667	95%

But this isn't why axe exists.

Token efficiency is a byproduct of precision. When you extract only the information needed to make a correct decision, you naturally use fewer tokens than dumping everything.

However, axe-dig is not a token-saving machine. When the situation demands it—when you need to trace a complex bug through multiple layers, when you need to understand how a feature connects throughout the codebase—axe-dig will fetch more context, not less.

The principle: Fetch exactly what's needed for technical precision. Sometimes that's 175 tokens. Sometimes it's 15,000 tokens. The difference is intentionality.

Other tools are incentivized to burn tokens (they charge per token). axe is incentivized to get the answer right.

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Built for Local Intelligence

axe was designed with local compute and local LLMs in mind.

Why does this matter?

Local LLMs have different constraints than cloud APIs:

• Slower prefill and decoding (can't waste time on irrelevant context)
• Smaller context windows (need precision, not bloat)
• No per-token billing (optimization is about speed and accuracy, not cost)

This forced us to build a precise retrieval engine from the ground up. We couldn't rely on "dump everything and let the cloud LLM figure it out."

The result: axe works brilliantly with both local and cloud models, because precision benefits everyone.

Running Locally: Real-World Performance

Here's axe running with srswti/blackbird-she-doesnt-refuse-21b—a 21B parameter model from our Bodega collection, running entirely locally:

Hardware: M1 Max, 64GB RAM
Model: Bodega Blackbird 21B (local inference)
Performance: Spawning subagents, parallel task execution, full agentic capabilities

As you can see, the capability of axe-optimized Bodega models running locally is exceptional. The precision retrieval engine means even local models can handle complex workflows efficiently—because they're not wasting compute on irrelevant context.

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The axe-dig Advantage: Semantic Search

Traditional search finds syntax. axe-dig semantic search finds behavior.

# Traditional grep
grep "cache" src/  # Finds: variable names, comments, "cache_dir"

# axe-dig semantic search
chop semantic search "memoize expensive computations with TTL expiration"

# Finds: get_user_profile() because:
# - It calls redis.get() and redis.setex() (caching pattern)
# - Has TTL parameter in redis.setex call
# - Called by functions that do expensive DB queries
# Even though it doesn't mention "memoize" or "TTL"

Every function gets embedded with:

• Signature and docstring
• Forward and backward call graphs
• Complexity metrics (branches, loops, cyclomatic complexity)
• Data flow patterns (variables used and transformed)
• Dependencies (imports, external modules)
• First ~10 lines of implementation

This gets encoded into 1024-dimensional embeddings, indexed with FAISS for fast similarity search.

Find code by what it does, not what it's named.

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Daemon Architecture: 300x Faster Queries

The old way: Every query spawns a new process, parses the entire codebase, builds indexes, returns result, exits. ~30 seconds.

axe-dig's daemon: Long-running background process with indexes in RAM. ~100ms.

Command	Daemon	CLI Spawn	Speedup
search	0.2ms	72ms	302x
extract	9ms	97ms	11x
impact	0.2ms	1,129ms	7,374x
structure	0.6ms	181ms	285x
Average	10ms	1,555ms	155x

Why impact shows 7,374x speedup: The CLI must rebuild the entire call graph from scratch on every invocation (~1.1 seconds). The daemon keeps the call graph in memory, so queries return in <1ms.

Incremental updates: When you edit one function, axe-dig doesn't re-analyze the entire codebase. Content-hash-based caching with automatic dependency tracking means 10x faster incremental updates.

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Real-World Workflow: Debugging a Production Bug

Scenario: Users sometimes get stale data even after updates.

# 1. Find where the error occurs
chop search "get_user" src/

# 2. Get the program slice (what code affects the return value?)
chop slice src/db.py get_user 45

# Output shows only the 6 relevant lines:
# 12:  cached = redis.get(f"user:{user_id}")
# 15:  if cached:
# 18:      return json.loads(cached)
# 23:  user = db.query("SELECT * FROM users WHERE id = ?", user_id)
# 34:  redis.setex(f"user:{user_id}", 3600, json.dumps(user))
# 45:  return user

# 3. Find who calls this (to see if cache is invalidated on update)
chop impact get_user src/

# 4. Search for update functions
chop semantic search "update user data in database" src/
# Finds update_user() but it doesn't call redis.delete()!

# 5. Check data flow in update_user
chop dfg src/db.py update_user
# Shows: updates DB but never invalidates cache

Result: Found the bug—cache invalidation missing in update_user(). 4 commands, 3 minutes.

Before axe-dig: Read multiple files, manually trace execution, spend 45 minutes debugging, maybe miss the issue entirely.

With axe-dig: Surgical precision. Confidence to ship the fix.

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Core Capabilities

Code Intelligence (powered by axe-dig)

Tool	What it does	Use case
CodeSearch	Semantic search by behavior	"Find payment processing logic"
CodeContext	LLM-ready function summaries with call graphs	Understand unfamiliar code
CodeStructure	Navigate functions/classes in files/dirs	Explore new codebases
CodeImpact	Reverse call graph (who calls this?)	Safe refactoring

File Operations

• ReadFile / WriteFile / StrReplaceFile - Standard file I/O
• Grep - Exact file locations + line numbers (use after CodeSearch)
• Glob - Pattern matching
• ReadMediaFile - Images, PDFs, videos

Multi-Agent Workflows

• Task - Spawn subagents for parallel work
• CreateSubagent - Custom agent specs
• SetTodoList - Track multi-step tasks

Subagents in action:

Spawn specialized subagents to divide and conquer complex workflows. Each subagent operates independently with its own context and tools.

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Quick Start

Install

# Install axe-cli (includes axe-dig)
uv pip install axe-cli

# Or from source
git clone https://github.com/SRSWTI/axe-cli
cd axe-cli
make prepare
make build

Run

cd /path/to/your/project
axe

On first run, axe-dig automatically indexes your codebase (30-60 seconds for typical projects). After that, queries are instant.

Start Using

# greet axe
hiii

# start coding
hey axe, can you tell me how does dwq targets are computed in mlx

# Toggle to shell mode
[Ctrl+X]
pytest tests/
[Ctrl+X]

Hit Ctrl+X to toggle between axe and your normal shell. No context switching. No juggling terminals.

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Powered by SRSWTI Inc.

Building the world's fastest retrieval and inference engines.

Bodega Inference Engine

Exclusive models trained/optimized for Bodega Inference Engine. axe includes zero-day support for all Bodega models, ensuring immediate access to our latest breakthroughs.

Note: Our models are also available on 🤗 Hugging Face.

Raptor Series

Ultra-compact reasoning models designed for efficiency and edge deployment. Super light, amazing agentic coding capabilities, robust tool support, minimal memory footprint.

• 🤗 bodega-raptor-0.9b - 900M params. Runs on base m4 air with 100+ tok/s.
• 🤗 bodega-raptor-90m - Extreme edge variant. Sub-100M params for amazing tool calling.
• 🤗 bodega-raptor-1b-reasoning-opus4.5-distill - Distilled from Claude Opus 4.5 reasoning patterns.
• 🤗 bodega-raptor-8b-mxfp4 - Balanced power/performance for laptops.
• 🤗 bodega-raptor-15b-6bit - Enhanced raptor variant.

Flagship Models

Frontier intelligence, distilled and optimized.

• 🤗 deepseek-v3.2-speciale-distilled-raptor-32b-4bit - DeepSeek V3.2 distilled to 32B with Raptor reasoning. Exceptional math/code generation in 5-7GB footprint. 120 tok/s on M1 Max.
• 🤗 bodega-centenario-21b-mxfp4 - Production workhorse. 21B params optimized for sustained inference workloads.
• 🤗 bodega-solomon-9b - Multimodal and best for agentic coding.

Axe-Turbo Series

Launched specifically for the Axe coding use case. High-performance agentic coding models optimized for the Axe ecosystem.

• 🤗 axe-turbo-1b - 1B params, 150 tok/s, sub-50ms first token. Edge-first architecture.
• 🤗 axe-turbo-31b - High-capacity workloads. Exceptional agentic capabilities.

Specialized Models

Task-specific optimization.

• 🤗 bodega-vertex-4b - 4B params. Optimized for structured data.
• 🤗 blackbird-she-doesnt-refuse-21b - Uncensored 21B variant for unrestricted generation.

Using Bodega Models

Configure Bodega in ~/.axe/config.toml:

default_model = "bodega-raptor"

[providers.bodega]
type = "bodega"
base_url = "http://localhost:44468"  # Local Bodega server
api_key = ""

[models.bodega-raptor]
provider = "bodega"
model = "srswti/bodega-raptor-8b-mxfp4"
max_context_size = 32768
capabilities = ["thinking"]

[models.bodega-turbo]
provider = "bodega"
model = "srswti/axe-turbo-31b"
max_context_size = 32768
capabilities = ["thinking"]

See sample_config.toml for more examples including OpenRouter, Anthropic, and OpenAI configurations.

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Documentation Index

We've organized the docs to make them digestible. Here's what's where:

Common Use Cases & Workflows

Learn how to use axe for implementing features, fixing bugs, understanding unfamiliar code, and automating tasks. Includes real workflow examples for debugging, refactoring, and exploration.

Built-in Tools

Complete reference for all available tools: file operations, shell commands, multi-agent tasks, and the axe-dig code intelligence tools. Every tool is designed for precision, not guesswork.

Agent Skills

How to create and use specialized skills to extend axe's capabilities. Skills are reusable workflows and domain expertise that you can invoke with /skill:name commands. Turn your team's best practices into executable knowledge.

Agents & Subagents

Guide to creating custom agents and spawning specialized subagents for parallel work. These subagents operate with precision to divide and conquer complex workflows.

Technical Reference

Deep dive into configuration (providers, models, loop control), session management, architecture, and MCP integration. Everything you need to customize axe for your workflow.

axe-dig: Code Intelligence Engine

The secret weapon. Complete documentation on axe-dig's 5-layer architecture, semantic search, daemon mode, and program slicing. Learn how to extract precise context while preserving everything needed for correct edits. Includes performance benchmarks, real-world debugging workflows, and the design rationale behind every choice. This is what makes axe different from every other coding tool.

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What's Coming

Our internal team has been using features that will change the game:

1. Interactive Dashboard: Visualize Your Codebase

Understanding code isn't just about reading—it's about seeing the structure, connections, and flow.

The dashboard provides real-time visualization for:

Code Health Analysis:

• Cyclic dependencies: Visualize circular imports and dependency loops that make refactoring dangerous
• Dead code detection: See unreachable functions and unused modules with connection graphs
• Safe refactoring zones: Identify code that can be changed without cascading effects
• Execution trace visualization: Watch the actual flow of data through your system at runtime

Debugging Workflows:

• Trace execution paths visually from entry point to crash
• See which functions are called, in what order, with what values
• Identify bottlenecks and performance hotspots in the call graph
• Understand data transformations across multiple layers

The dashboard turns axe-dig's 5-layer analysis into interactive, explorable visualizations. No more drawing diagrams on whiteboards—axe generates them from your actual code.

2. Execution Tracing

See what actually happened at runtime. No more guessing why a test failed.

# Trace a failing test
/trace pytest tests/test_payment.py::test_refund

# Shows exact values that flowed through each function:
# process_refund(amount=Decimal("50.00"), transaction_id="tx_123")
#   → validate_refund(transaction=Transaction(status="completed"))
#     → check_refund_window(created_at=datetime(2024, 1, 15))
#       → datetime.now() - created_at = timedelta(days=45)
#       → raised RefundWindowExpired  # ← 30-day window exceeded

3. Monorepo Factoring (Enterprise Feature)

Status: Under active development. Our team has been using this internally for weeks.

Large monorepos become unmaintainable when everything is tangled together. axe analyzes your codebase and automatically factors it into logical modules based on:

• Dependency analysis: Which code actually depends on what
• Call graph clustering: Functions that work together, grouped together
• Data flow boundaries: Natural separation points in your architecture
• Usage patterns: How different parts of the codebase are actually used

The result: Clear module boundaries, reduced coupling, easier maintenance. This has been heavily requested by enterprise customers managing multi-million-line monorepos.

Example workflow:

# Analyze current structure
/monorepo analyze .

# Shows: 47 logical modules detected across 1,200 files
# Suggests: Split into 5 packages with clear boundaries
# Impact: Reduces cross-module dependencies by 73%

# Apply factoring
/monorepo factor --target packages/

4. Language Migration (X → Y)

Migrating codebases between languages is notoriously error-prone. axe uses its deep understanding of code structure to enable reliable migrations:

How it works:

1. Analyze source code: Extract call graphs, data flow, and business logic
2. Preserve semantics: Understand what the code does, not just what it says
3. Generate target code: Translate to the new language while maintaining behavior
4. Verify correctness: Compare execution traces and test coverage

Supported migrations:

• Python → TypeScript (preserve type safety)
• JavaScript → Go (maintain concurrency patterns)
• Ruby → Rust (keep performance characteristics)
• Java → Kotlin (modernize while preserving architecture)

Unlike simple transpilers, axe understands your code's intent and translates it idiomatically to the target language.

5. Performance Debugging

Flame graphs and memory profiling integrated directly in the chat interface.

# Generate flame graph
/flamegraph api_server.py

# Find memory leaks
/memory-profile background_worker.py

6. Smart Test Selection

# Only run tests affected by your changes
/test-impact src/payment/processor.py

# Shows: 12 tests need to run (not all 1,847)

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Supported Languages

Python, TypeScript, JavaScript, Go, Rust, Java, C, C++, Ruby, PHP, C#, Kotlin, Scala, Swift, Lua, Elixir

Language auto-detected. Specify with --lang if needed.

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Comparison

Feature	Claude Code	OpenAI Codex	axe
Built for	Weekend projects	Demos	Production codebases
Context strategy	Dump everything	Dump everything	Extract signal (precision-first)
Code search	Text/regex	Text/regex	Semantic (behavior-based)
Call graph analysis	❌	❌	✅ 5-layer analysis
Precision optimization	❌ (incentivized to waste)	❌ (incentivized to waste)	✅ Fetch what's needed for correctness
Execution tracing	❌	❌	✅ Coming soon
Flame graphs	❌	❌	✅ Coming soon
Memory profiling	❌	❌	✅ Coming soon
Visual debugging	❌	❌	✅ Coming soon
Shell integration	❌	❌	✅ Ctrl+X toggle
Session management	Limited	Limited	✅ Full history + replay
Skills system	❌	❌	✅ Modular, extensible
Subagents	❌	❌	✅ Parallel task execution
Battle-tested	Public beta	Public API	6 months internal use

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Community

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Docs: Full documentation

Acknowledgements

Special thanks to MoonshotAI/kimi-cli for their amazing work, which inspired our tools and the Kosong provider.